2200940
2023-04-26
Introduction:
Task: To perform a full data visualisation on the policing dataset from Dallas, Texas in 2016.
In this analysis, I will examine the policing dataset from Dallas, Texas that has been made available by the Center for Policing Equity. The dataset comprises 47 columns and 2384 rows, containing diverse information about each incident, including the date and time of the occurrence, officer and subject ID, officer and subject gender and race, geographical location such as street number and name, location district, city, state, latitude and longitude, and different types of force employed. Furthermore, the dataset encompasses columns that provide information regarding injuries sustained by both officers and subjects, as well as whether or not the subject was taken into custody.
I will be performing a full data visualization on the policing dataset, including tables, charts, plots, and maps, as well as advanced graphics. The main goal is to explore the data in creative ways and present a “story” in report that sheds light on police behavior and provides insights about racial equity in policing.
The report will be structured as follows:
- Data Description and Pre-processing
- Data Visualization
- Conclusion
Required Libraries:
options(repos = c(CRAN = "https://cloud.r-project.org"))
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.2.3library(ggforce)## Warning: package 'ggforce' was built under R version 4.2.3install.packages("plotly")## Installing package into 'C:/Users/Thara Jeni/AppData/Local/R/win-library/4.2'
## (as 'lib' is unspecified)## package 'plotly' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\Thara Jeni\AppData\Local\Temp\Rtmp8ishu2\downloaded_packageslibrary(plotly)## Warning: package 'plotly' was built under R version 4.2.3##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary(broom)## Warning: package 'broom' was built under R version 4.2.3library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.2.3## Warning: package 'forcats' was built under R version 4.2.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.3
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.0 ✔ tibble 3.1.8
## ✔ purrr 0.3.5 ✔ tidyr 1.2.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks plotly::filter(), stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorslibrary(dplyr) Data Description and Preprocessing
In this section, I have provided the detailed description of the policing dataset as step-by-step.First, printing the dimension of the data, dropping unnecessary row used domain knowledge, next understanding the attributes and summary of data and finally printing the dataset.
Here, the dataset used in this report contains 2384 rows and 47 columns.
#reading data
data<-read.csv(file="C:/Users/Thara Jeni/Desktop/Data_Viz.csv")
dim(data)## [1] 2384 47Here, we are dropping an unnecessary row with domain knowledge in order to proceed with the data analysis.
# droping a unnecessary row
data_new<-data[-1,]
dim(data_new)## [1] 2383 47Numerical representation
Her, doing numerical representation of the policing data by attributes and summary functions:
- The attributes function gives all required informations all such as names, row names, and dimensions.
- The summary function gives the summary statistics of the data, such as minimum, maximum, mean, and quartiles.
attributes(data_new)## $names
## [1] "INCIDENT_DATE"
## [2] "INCIDENT_TIME"
## [3] "UOF_NUMBER"
## [4] "OFFICER_ID"
## [5] "OFFICER_GENDER"
## [6] "OFFICER_RACE"
## [7] "OFFICER_HIRE_DATE"
## [8] "OFFICER_YEARS_ON_FORCE"
## [9] "OFFICER_INJURY"
## [10] "OFFICER_INJURY_TYPE"
## [11] "OFFICER_HOSPITALIZATION"
## [12] "SUBJECT_ID"
## [13] "SUBJECT_RACE"
## [14] "SUBJECT_GENDER"
## [15] "SUBJECT_INJURY"
## [16] "SUBJECT_INJURY_TYPE"
## [17] "SUBJECT_WAS_ARRESTED"
## [18] "SUBJECT_DESCRIPTION"
## [19] "SUBJECT_OFFENSE"
## [20] "REPORTING_AREA"
## [21] "BEAT"
## [22] "SECTOR"
## [23] "DIVISION"
## [24] "LOCATION_DISTRICT"
## [25] "STREET_NUMBER"
## [26] "STREET_NAME"
## [27] "STREET_DIRECTION"
## [28] "STREET_TYPE"
## [29] "LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION"
## [30] "LOCATION_CITY"
## [31] "LOCATION_STATE"
## [32] "LOCATION_LATITUDE"
## [33] "LOCATION_LONGITUDE"
## [34] "INCIDENT_REASON"
## [35] "REASON_FOR_FORCE"
## [36] "TYPE_OF_FORCE_USED1"
## [37] "TYPE_OF_FORCE_USED2"
## [38] "TYPE_OF_FORCE_USED3"
## [39] "TYPE_OF_FORCE_USED4"
## [40] "TYPE_OF_FORCE_USED5"
## [41] "TYPE_OF_FORCE_USED6"
## [42] "TYPE_OF_FORCE_USED7"
## [43] "TYPE_OF_FORCE_USED8"
## [44] "TYPE_OF_FORCE_USED9"
## [45] "TYPE_OF_FORCE_USED10"
## [46] "NUMBER_EC_CYCLES"
## [47] "FORCE_EFFECTIVE"
##
## $row.names
## [1] 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## [15] 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## [29] 30 31 32 33 34 35 36 37 38 39 40 41 42 43
## [43] 44 45 46 47 48 49 50 51 52 53 54 55 56 57
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## [1989] 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
## [2003] 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
## [2017] 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031
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## [2367] 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381
## [2381] 2382 2383 2384
##
## $class
## [1] "data.frame"summary(data_new)## INCIDENT_DATE INCIDENT_TIME UOF_NUMBER OFFICER_ID
## Length:2383 Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## OFFICER_GENDER OFFICER_RACE OFFICER_HIRE_DATE
## Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## OFFICER_YEARS_ON_FORCE OFFICER_INJURY OFFICER_INJURY_TYPE
## Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## OFFICER_HOSPITALIZATION SUBJECT_ID SUBJECT_RACE
## Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## SUBJECT_GENDER SUBJECT_INJURY SUBJECT_INJURY_TYPE SUBJECT_WAS_ARRESTED
## Length:2383 Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## SUBJECT_DESCRIPTION SUBJECT_OFFENSE REPORTING_AREA BEAT
## Length:2383 Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## SECTOR DIVISION LOCATION_DISTRICT STREET_NUMBER
## Length:2383 Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## STREET_NAME STREET_DIRECTION STREET_TYPE
## Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION LOCATION_CITY
## Length:2383 Length:2383
## Class :character Class :character
## Mode :character Mode :character
## LOCATION_STATE LOCATION_LATITUDE LOCATION_LONGITUDE INCIDENT_REASON
## Length:2383 Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## REASON_FOR_FORCE TYPE_OF_FORCE_USED1 TYPE_OF_FORCE_USED2 TYPE_OF_FORCE_USED3
## Length:2383 Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## TYPE_OF_FORCE_USED4 TYPE_OF_FORCE_USED5 TYPE_OF_FORCE_USED6
## Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## TYPE_OF_FORCE_USED7 TYPE_OF_FORCE_USED8 TYPE_OF_FORCE_USED9
## Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## TYPE_OF_FORCE_USED10 NUMBER_EC_CYCLES FORCE_EFFECTIVE
## Length:2383 Length:2383 Length:2383
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :characterHere, displaying the first few rows of the dataset which can provide a quick overview of the dataset.
head(data_new, 5)## INCIDENT_DATE INCIDENT_TIME UOF_NUMBER OFFICER_ID OFFICER_GENDER
## 2 9/3/16 4:14:00 AM 37702 10810 Male
## 3 3/22/16 11:00:00 PM 33413 7706 Male
## 4 5/22/16 1:29:00 PM 34567 11014 Male
## 5 1/10/16 8:55:00 PM 31460 6692 Male
## 6 11/8/16 2:30:00 AM 37879, 37898 9844 Male
## OFFICER_RACE OFFICER_HIRE_DATE OFFICER_YEARS_ON_FORCE OFFICER_INJURY
## 2 Black 5/7/14 2 No
## 3 White 1/8/99 17 Yes
## 4 Black 5/20/15 1 No
## 5 Black 7/29/91 24 No
## 6 White 10/4/09 7 No
## OFFICER_INJURY_TYPE OFFICER_HOSPITALIZATION SUBJECT_ID SUBJECT_RACE
## 2 No injuries noted or visible No 46424 Black
## 3 Sprain/Strain Yes 44324 Hispanic
## 4 No injuries noted or visible No 45126 Hispanic
## 5 No injuries noted or visible No 43150 Hispanic
## 6 No injuries noted or visible No 47307 Black
## SUBJECT_GENDER SUBJECT_INJURY SUBJECT_INJURY_TYPE
## 2 Female Yes Non-Visible Injury/Pain
## 3 Male No No injuries noted or visible
## 4 Male No No injuries noted or visible
## 5 Male Yes Laceration/Cut
## 6 Male No No injuries noted or visible
## SUBJECT_WAS_ARRESTED SUBJECT_DESCRIPTION SUBJECT_OFFENSE
## 2 Yes Mentally unstable APOWW
## 3 Yes Mentally unstable APOWW
## 4 Yes Unknown APOWW
## 5 Yes FD-Unknown if Armed Evading Arrest
## 6 Yes Unknown Other Misdemeanor Arrest
## REPORTING_AREA BEAT SECTOR DIVISION LOCATION_DISTRICT STREET_NUMBER
## 2 2062 134 130 CENTRAL D14 211
## 3 1197 237 230 NORTHEAST D9 7647
## 4 4153 432 430 SOUTHWEST D6 716
## 5 4523 641 640 NORTH CENTRAL D11 5600
## 6 2167 346 340 SOUTHEAST D7 4600
## STREET_NAME STREET_DIRECTION STREET_TYPE
## 2 Ervay N St.
## 3 Ferguson NULL Rd.
## 4 bimebella dr NULL Ln.
## 5 LBJ NULL Frwy.
## 6 Malcolm X S Blvd.
## LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION LOCATION_CITY LOCATION_STATE
## 2 211 N ERVAY ST Dallas TX
## 3 7647 FERGUSON RD Dallas TX
## 4 716 BIMEBELLA LN Dallas TX
## 5 5600 L B J FWY Dallas TX
## 6 4600 S MALCOLM X BLVD Dallas TX
## LOCATION_LATITUDE LOCATION_LONGITUDE INCIDENT_REASON REASON_FOR_FORCE
## 2 32.782205 -96.797461 Arrest Arrest
## 3 32.798978 -96.717493 Arrest Arrest
## 4 32.73971 -96.92519 Arrest Arrest
## 5 Arrest Arrest
## 6 Arrest Arrest
## TYPE_OF_FORCE_USED1 TYPE_OF_FORCE_USED2 TYPE_OF_FORCE_USED3
## 2 Hand/Arm/Elbow Strike
## 3 Joint Locks
## 4 Take Down - Group
## 5 K-9 Deployment
## 6 Verbal Command Take Down - Arm
## TYPE_OF_FORCE_USED4 TYPE_OF_FORCE_USED5 TYPE_OF_FORCE_USED6
## 2
## 3
## 4
## 5
## 6
## TYPE_OF_FORCE_USED7 TYPE_OF_FORCE_USED8 TYPE_OF_FORCE_USED9
## 2
## 3
## 4
## 5
## 6
## TYPE_OF_FORCE_USED10 NUMBER_EC_CYCLES FORCE_EFFECTIVE
## 2 NULL Yes
## 3 NULL Yes
## 4 NULL Yes
## 5 NULL Yes
## 6 NULL No, YesData Visualization
Table
I opted for analyzing the “SUBJECT_DESCRIPTION” column from the table to gain valuable insights as it can reveal information about the nature of police use of force incidents. This column provides details about the behavior or actions of the individuals involved in these incidents, which can shed light on the contributing factors that lead to police use of force. For instance, a significant proportion of incidents involving individuals under the influence of drugs or alcohol may suggest a link between substance abuse and police use of force. Additionally, a high number of incidents involving unarmed or mentally ill individuals may indicate a need for better training and resources to handle such situations more effectively.
table(data_new$SUBJECT_DESCRIPTION)##
## Alchohol Alchohol and unknown drugs
## 382 280
## Animal FD-Animal
## 1 1
## FD-Motor Vehicle FD-Suspect Unarmed
## 2 29
## FD-Suspect w/ Gun FD-Suspect w/ Other Weapon
## 36 25
## FD-Unknown if Armed Marijuana
## 110 50
## Mentally unstable None detected
## 412 297
## NULL Unknown
## 76 364
## Unknown Drugs
## 318Explanation of the above table: Based on the table, we can observe the different categories and their respective counts in the column.It is evident that the most frequent subject description provided is “None detected,” with a count of 412. This suggests that in the majority of incidents involving police use of force, the subjects were not under the influence of any substance.Following closely in second place is the category “Alcohol” with a count of 382, indicating that incidents involving the use of force by police officers often involve individuals who have consumed alcohol.The third most common category is “Unknown drugs,” with a count of 318, signifying that drugs are frequently involved in such incidents, but the specific type of drug remains unidentified.
Two-way table
I have selected “OFFICER_RACE” and “OFFICER_GENDER” columns to create a two-way table that provides valuable insights into the diversity and representation of different races and genders within the police force. By analyzing this table, we can determine the number of male and female police officers within each racial category. This information can help us identify any potential gender or racial inequalities within the police force, which may have significant implications for community relations and policing practices.
table(data_new$OFFICER_RACE, data_new$OFFICER_GENDER)##
## Female Male
## American Ind 2 6
## Asian 7 48
## Black 49 292
## Hispanic 42 440
## Other 6 21
## White 134 1336Explanation of the above two-way table: This two-way examines the potential correlation between the race and gender of police officers involved in use-of-force incidents. The analysis presents a breakdown of the number of police officers from each racial category (American Indian, Asian, Black, Hispanic, Other, and White) and gender (Female or Male) involved in these situations. As per the findings, the majority of police officers involved in use-of-force incidents were male, with a total of 2144, while only 231 female police officers were involved.Regarding the racial distribution of police officers involved in these incidents, the data shows that White police officers were involved in the most use-of-force situations, with a total of 1470. Following this, Hispanic officers were involved in 482 incidents, Black officers in 341 incidents, Asian officers in 55 incidents, American Indian officers in eight incidents, and officers of other races were involved in 27 incidents.In summary, this table provides valuable insights into the demographic composition of police officers involved in use-of-force incidents, enabling us to gain a better understanding of the influence of race and gender on police interactions with the public.
Creating a bar plot
Here, I have choosed two columns to used in the plot are “OFFICER_ID” and “OFFICER_RACE”. “OFFICER_ID” was used to count the number of officers for each race, while “OFFICER_RACE” was used as the x-axis variable for the bar plot to display the different racial categories. The purpose of this visualization is to provide an overview of the racial diversity among the police force in the dataset.
# Create a data frame with officer ID and race
df <- data.frame(OFFICER_ID = data_new$OFFICER_ID, OFFICER_RACE = data_new$OFFICER_RACE)
# Create a summary data frame with counts of officers by race
summary_df <- aggregate(OFFICER_ID ~ OFFICER_RACE, data = df, FUN = length)
# Create a bar plot of officer counts by race
ggplot(summary_df, aes(x = OFFICER_RACE, y = OFFICER_ID, fill = OFFICER_RACE)) +
geom_bar(stat = "identity") +
xlab("Officer Race") +
ylab("Number of Officers") +
ggtitle("Number of Officers by Race") +
theme(plot.title = element_text(hjust = 0.5))
Explanation of the above bar plot: The bar graph depicts the
number of officers in each racial group. The x-axis indicates the
various officer races, and the y-axis represents the number of officers
in each category. The plot’s bars are coloured according to the
officers’ ethnicity, and the legend on the right-hand side of the plot
reveals the color-coding for each racial category.The plot shows that
the majority of officers, 1,470 in total, are White. The second largest
category, with 341 officers, is Black officers. In comparison, the other
racial groups have less officers, including American Indian, Asian,
Hispanic, and Other.This graph might help you comprehend the racial
composition of police enforcement.
Creating a histogram
I used the UOF_NUMBER column for the histogram in this case to better understand the distribution of the number of times cops use force in various scenarios. The histogram provides insight into how frequently officers use force by presenting the frequency of the UOF_NUMBER values, as well as any potential outliers or patterns in the data. This can be beneficial in identifying locations where cops may be using excessive force or where extra training is required. Understanding the distribution of UOF_NUMBER values can also assist policymakers and law enforcement organisations in making judgements about use of force regulations and training programmes.
# Convert UOF_NUMBER column to numeric
data_new$UOF_NUMBER <- as.numeric(data_new$UOF_NUMBER)## Warning: NAs introduced by coercion# Create a histogram for UOF_NUMBER
hist(data_new$UOF_NUMBER, col = "blue", main = "UOF_NUMBER Histogram",
xlab = "UOF_NUMBER", ylab = "Frequency")
Explanation of the above histogram: The histogram represents
the distribution of the UOF_NUMBER variable in the dataset. The x-axis
represents the range of UOF_NUMBER values in the dataset and the y-axis
represents the frequency of the values within each range. The histogram
is divided into bins or intervals, with each bin representing a range of
UOF_NUMBER values.From the plot, we can see that the UOF_NUMBER values
are concentrated in the lower end of the range, with most values falling
between 0 and 5. There are also some outliers on the higher end of the
range, with a few values reaching up to 20. The blue color in the
histogram represents the frequency of UOF_NUMBER values within each bin.
Overall, this histogram provides an overview of the distribution of
UOF_NUMBER values in the dataset, which can be useful in identifying any
patterns or outliers in the data.
Creating a Pie chart
Here, I have choosed the “DIVISION” column for the pie chart beacause to visualize the proportion of offenses committed by each division of the police department. This can provide insights into which division may require more attention or resources for improving their procedures and protocols to minimize the number of offenses committed. Additionally, this can help identify any patterns or trends in the types of offenses committed by each division. Overall, the pie chart can provide a quick and easy-to-understand visual representation of the distribution of offenses across different divisions.
# Create a table of counts for each offense type
offense_counts <- table(data_new$DIVISION)
# Create a pie chart for the Division
pie(offense_counts, main = "Offense on Subjects via Division", col = rainbow(length(offense_counts)))
Explanation of the above histogram: The pie chart shows the
distribution of offenses on subjects via division. The chart is divided
into several sections, each representing a different division. The size
of each section corresponds to the proportion of offenses in that
division. The chart uses a rainbow color scheme to differentiate between
the different divisions.
From the chart, it is clear that the majority of offenses on subjects were committed by the Central division, which accounts for nearly half of all offenses. The Northern and Western divisions also have a significant number of offenses, each accounting for around a quarter of all offenses. The remaining divisions (Southern, Southeastern, and Southwestern) account for a relatively small proportion of offenses on subjects.
Overall, the pie chart provides a quick and easy-to-understand visual representation of the distribution of offenses by division. It allows viewers to easily compare the proportion of offenses in each division and identify any divisions that have a particularly high or low number of offenses.
Creating the dot plot
Here, I have choosed the “OFFICER_HOSPITALIZATION” and “SUBJECT_DESCRIPTION” columns for the dot plot because to visualize the relationship between the two variables, the goal is to determine whether there is any correlation between the officer’s hospitalization and the subject’s description during the use of force incidents. The dot plot is an effective way to identify patterns and trends in the data and to quickly see any potential relationships between the variables.
# Create a subset of the data with non-missing values for OFFICER_HOSPITALIZATION and SUBJECT_DESCRIPTION
data_new_subset <- data_new[!is.na(data_new$OFFICER_HOSPITALIZATION) & !is.na(data_new$SUBJECT_DESCRIPTION),]
# Create the dot plot
ggplot(data_new_subset, aes(x = OFFICER_HOSPITALIZATION, y = SUBJECT_DESCRIPTION)) +
geom_point(size = 3, alpha = 0.5) +
ggtitle("Officer Hospitalization vs Subject Description") +
xlab("Officer Hospitalization") +
ylab("Subject Description")
Explanation of the above dot plot: The dot plot is showing the
relationship between two variables: officer hospitalization and subject
description. Each dot on the plot represents a unique combination of the
two variables. The x-axis represents the officer hospitalization
variable, which is a binary variable indicating whether the officer was
hospitalized or not. The y-axis represents the subject description
variable, which provides a description of the type of subject involved
in the use of force incident.
The dot plot shows that most of the incidents involved subjects who were not known to have any substance in their system or who had no substance detected. However, there are also incidents where the subject was under the influence of alcohol or drugs, and in some of these cases, the officer was hospitalized. The dot plot also shows that incidents involving mentally unstable subjects resulted in a higher rate of officer hospitalization. Overall, the dot plot provides a useful visual representation of the relationship between these two variables.
Create the density plot
Here, I have choosed the “SUBJECT_RACE” column for the density plot is to visualize the distribution of the racial makeup of the subjects involved in use of force incidents. The density plot shows the relative density of the different racial groups, allowing for an understanding of the proportions of each group.
# Create a subset of the data with non-missing values for STREET_NUMBER
data_new_subset <- data_new[!is.na(data_new$SUBJECT_RACE),]
# Create the density plot
ggplot(data_new_subset, aes(x = SUBJECT_RACE)) +
geom_density(fill = "blue", alpha = 0.3) +
ggtitle("Density Plot on SUBJECT RACE") +
xlab("SUBJECT RACE") +
ylab("Density")## Warning: Groups with fewer than two data points have been dropped.## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
Explanation of the above density plot: The plot shows the
density of the different races represented in the dataset, with the
x-axis representing the race categories and the y-axis representing the
density of the variable. The plot is filled with a blue color and has an
alpha of 0.3, making it easier to distinguish the density of different
categories. The plot also has a title, “Density Plot on SUBJECT RACE”,
as well as labels for the x and y axis, making it easier to understand
the content of the plot.
The density plot can be used to gain insights about the distribution of races in the dataset, such as which races are more or less represented, and the distribution of the variable across the different races. This information can be used to inform further analysis, identify potential biases or disparities in the data, and help draw conclusions about the research question being studied. Overall, the output of the code is a useful visualization for analyzing and communicating the distribution of races in the dataset.
Creating a correlation plot
The purpose of correlation analysis is to identify patterns and relationships between pairs of numerical variables, so the columns chosen should be numerical and have some reasonable expectation of being related to each other. Ultimately, the choice of which numerical columns to include in a correlation analysis will depend on the research question, the available data, and the researcher’s domain knowledge and intuition.
Converting all numerical columns to numeric datatype fron character datatype
num_col1 <- as.numeric(data_new$UOF_NUMBER)
num_col2 <- as.numeric(data_new$OFFICER_ID)
num_col3 <- as.numeric(data_new$OFFICER_YEARS_ON_FORCE)
num_col4 <- as.numeric(data_new$SUBJECT_ID)
num_col5 <- as.numeric(data_new$REPORTING_AREA)
num_col6 <- as.numeric(data_new$BEAT)
num_col7 <- as.numeric(data_new$SECTOR)
num_col8 <- as.numeric(data_new$STREET_NUMBER)
num_col9 <- as.numeric(data_new$LOCATION_LATITUDE)
num_col10 <- as.numeric(data_new$LOCATION_LONGITUDE)Selecting all numeric converted datatype columns into new dataset
numerical_col<-data.frame(num_col1,num_col2,num_col3,num_col4,num_col5,num_col6,num_col7,num_col8,num_col9,num_col10)
str(numerical_col)## 'data.frame': 2383 obs. of 10 variables:
## $ num_col1 : num 37702 33413 34567 31460 NA ...
## $ num_col2 : num 10810 7706 11014 6692 9844 ...
## $ num_col3 : num 2 17 1 24 7 7 7 9 4 8 ...
## $ num_col4 : num 46424 44324 45126 43150 47307 ...
## $ num_col5 : num 2062 1197 4153 4523 2167 ...
## $ num_col6 : num 134 237 432 641 346 235 132 515 133 614 ...
## $ num_col7 : num 130 230 430 640 340 230 130 510 130 610 ...
## $ num_col8 : num 211 7647 716 5600 4600 ...
## $ num_col9 : num 32.8 32.8 32.7 NA NA ...
## $ num_col10: num -96.8 -96.7 -96.9 NA NA ...dim(numerical_col)## [1] 2383 10colSums(is.na(numerical_col))## num_col1 num_col2 num_col3 num_col4 num_col5 num_col6 num_col7 num_col8
## 1636 0 0 0 0 0 0 0
## num_col9 num_col10
## 55 55# Remove rows with missing values
numerical_col_complete <- na.omit(numerical_col)
# Create a correlation matrix
cor_matrix <- cor(numerical_col_complete)
# Create a correlation plot
library(corrplot)## corrplot 0.92 loadedcorrplot(cor_matrix, method = "color", type = "lower", tl.cex = 0.8, tl.col = "black")
Explanation of the above density plot: The resulting plot can
be used to gain insights about the relationships between pairs of
numerical variables in the dataset. For example, a high positive
correlation between two variables suggests that they tend to increase or
decrease together, while a high negative correlation suggests that they
tend to move in opposite directions. A low or no correlation suggests
that the variables are not related. By examining the correlation matrix,
researchers can identify potential patterns or relationships in the data
that may be of interest for further analysis or exploration. Overall,
the output of the code is a useful tool for understanding the
relationships between numerical variables in the dataset.
Create Sina plot
the Sina plot can be visually appealing and can help to convey complex information in an easy-to-understand way. It is particularly useful when dealing with large datasets or when trying to compare multiple groups or variables simultaneously.
df<-data_new
# Convert incident date to date format
df$INCIDENT_DATE <- as.Date(df$INCIDENT_DATE, "%m/%d/%Y %H:%M:%S")
# Extract only date component of incident date
df$INCIDENT_DATE <- as.Date(df$INCIDENT_DATE)
# Create Sina plot
ggplot(df, aes(x = SUBJECT_WAS_ARRESTED, y = OFFICER_HOSPITALIZATION, fill = SUBJECT_WAS_ARRESTED)) +
geom_sina(aes(group = SUBJECT_WAS_ARRESTED), alpha = 0.8) +
labs(x = "Subject Was Arrested", y = "Officer Hospitalization", fill = "Subject Was Arrested") +
scale_fill_manual(values = c("#E69F00", "#56B4E9","#003f5c")) +
theme_bw()
Explanation of the above sina plot: Overall, the plot shows
that the distribution of “Officer Hospitalization” values is shifted to
the right for the “Yes” level of “Subject Was Arrested” compared to the
other levels. This suggests that there may be a higher rate of officer
hospitalization when a subject is arrested. However, it is important to
note that this plot only shows a correlation between the two variables
and cannot be used to determine causation.
Creating a Violin plot
The Violin plot displays the distribution of the “OFFICER_YEARS_ON_FORCE” variable for each unique “OFFICER_ID”.
data_new$OFFICER_ID<-as.numeric(data_new$OFFICER_ID)
data_new$OFFICER_YEARS_ON_FORCE<-as.numeric(data_new$OFFICER_YEARS_ON_FORCE)
ggplot(data_new, aes(x = OFFICER_ID, y = OFFICER_YEARS_ON_FORCE)) +
geom_violin() +
labs(x = "Officer ID", y = "Years on Force")
Explanation of the above sina plot: The Violin plot shows the
distribution of the data using a kernel density estimate (KDE) and a box
plot. The wider sections of the plot represent areas where the data is
more dense, and the narrower sections represent areas where the data is
less dense. The box plot inside the violin plot displays the median (the
line in the middle), the interquartile range (the box), and the range of
the data (the whiskers). Any points outside of the whiskers are
considered outliers.
The Violin plot is useful for visualizing the distribution of “OFFICER_YEARS_ON_FORCE” for each “OFFICER_ID” and can help to identify any patterns or trends in the data. In this specific case, we can see that the distribution of years on force is somewhat similar across officers, with a few outliers with significantly higher years on force.
Overall, the Violin plot is a useful tool for exploring the relationship between two variables and understanding the distribution of data within each category.
Creating a box plot
Here, for box plot I have selected OFFICER_YEARS_ON_FORCE and OFFICER_INJURY,
# Create a box plot of officer years on force by officer injury
boxplot(data_new$OFFICER_YEARS_ON_FORCE ~ data_new$OFFICER_INJURY,
main = "Officer Years on Force by Officer Injury",
xlab = "Officer Injury",
ylab = "Officer Years on Force",
col = c("lightblue", "pink"))
Creating a scatter plot
Here, I have selected OFFICER_HOSPITALIZATION and OFFICER_RACE.
ggplot(data_new, aes(x = OFFICER_HOSPITALIZATION, y = OFFICER_RACE)) +
geom_point() +
labs(title = "Scatter Plot of Officer Hospitalization and Race",
x = "Officer Hospitalization",
y = "Officer Race")
Creating a pair plot
# Subset numerical columns from your data frame
num_cols <- data.frame(num_col1, num_col2, num_col3, num_col4, num_col5)
# Create a pair plot
pairs(num_cols)
creating time series plot
# Convert INCIDENT_DATE to date format
data_new$INCIDENT_DATE <- as.Date(data_new$INCIDENT_DATE, format = "%m/%d/%Y")
# Create time series object
data_ts <- ts(data_new$REPORTING_AREA, start = c(2021, 1), end = c(2023, 4), frequency = 12)
# Create time series plot
plot(data_ts, main = "Time Series Plot of REPORTING_AREA", xlab = "Date", ylab = "REPORTING_AREA")
Creating a scatter plot with a smoothed curve
# Convert incident date to a date object
data_new$INCIDENT_DATE <- as.Date(data_new$INCIDENT_DATE, "%m/%d/%Y")
# Calculate counts by incident date
counts <- aggregate(data_new$UOF_NUMBER, by = list(date = data_new$INCIDENT_DATE), FUN = length)
# Create plot
ggplot(counts, aes(x = date, y = x)) +
geom_point() +
stat_smooth(method = "loess", span = 0.2)## `geom_smooth()` using formula = 'y ~ x'
Creating a leaflet map
library(leaflet)## Warning: package 'leaflet' was built under R version 4.2.3data_new$BEAT<-as.numeric(data_new$BEAT)
data_new$SECTOR<-as.numeric(data_new$SECTOR)
# Create a leaflet map
m <- leaflet(data_new) %>% addTiles()
# Add markers for each incident location
m <- m %>% addMarkers(~BEAT, ~SECTOR, popup = ~as.character(DIVISION))
# Display the map
mCreating Interactive plots
# Convert incident date to a date object
data_new$INCIDENT_DATE <- as.Date(data_new$INCIDENT_DATE, "%m/%d/%Y")
# Calculate counts by incident date
counts <- aggregate(data_new$UOF_NUMBER, by = list(date = data_new$INCIDENT_DATE), FUN = length)
# Create interactive plot
fig <- plot_ly(counts, x = ~date, y = ~x, type = "scatter", mode = "lines+markers") %>%
layout(
title = "Use of Force Incidents Over Time",
xaxis = list(title = "Incident Date"),
yaxis = list(title = "Count of UOF Incidents")
)
# Show plot
figConclusion
From all the above seen analysis, it would be very helpful for Dallas police to conduct the investigation in the precise manner. I have visualized analysis for different attributes , so that it is giving good insights for minor things. I hope that these multi-graphical representations with multi attributes in different aspects, would be helpful for future investigation.